Published in

Elsevier, Applied Mathematics and Computation, (227), p. 359-369, 2014

DOI: 10.1016/j.amc.2013.11.048

Links

Tools

Export citation

Search in Google Scholar

A geometrical approach to iterative isotone regression

Journal article published in 2014 by Arnaud Guyader, Nicolas Jégou, Alexander B. Németh, Sándor Z. Németh
This paper is available in a repository.
This paper is available in a repository.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Orange circle
Postprint: archiving restricted
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

Abstract

25 pages, 5 figures ; International audience ; In the present paper, we propose and analyze a novel method for estimating a univariate regression function of bounded variation. The underpinning idea is to combine two classical tools in nonparametric statistics, namely isotonic regression and the estimation of additive models. A geometrical interpretation enables us to link this iterative method with Von Neumann's algorithm. Moreover, making a connection with the general property of isotonicity of projection onto convex cones, we derive another equivalent algorithm and go further in the analysis. As iterating the algorithm leads to overfitting, several practical stopping criteria are also presented and discussed.